Sculpting Data for ML: The first act of Machine Learning

In the contemporary world of Artificial Intelligence and Machine Learning, data is the new oil. For Machine Learning algorithms to work their magic, it is imperative to lay a firm foundation with relevant data. Sculpting Data for ML introduces the readers to the first act of Machine Learning, Dataset Curation. This book puts forward practical tips to identify valuable information from the extensive amount of crude data available at our fingertips. The step-by-step guide accompanies code examples in Python from the extraction of real-world datasets and illustrates ways to hone the skills of extracting meaningful datasets. In addition, the book also dives deep into how data fits into the Machine Learning ecosystem and tries to highlight the impact good quality data can have on the Machine Learning system’s performance.

What’s Inside?

• Significance of data in Machine Learning
• Identification of relevant data signals
• End-to-end process of data collection and dataset construction
• Overview of extraction tools like BeautifulSoup and Selenium
• Step-by-step guide with Python code examples of real-world use cases
• Synopsis of Data Preprocessing and Feature Engineering techniques
• Introduction to Machine Learning paradigms from a data perspective

This book is for Machine Learning researchers, practitioners, or enthusiasts who want to tackle the data availability challenges to address real-world problems.

The authors Jigyasa Grover & Rishabh Misra are Machine Learning Engineers by profession and are passionate about tackling real-world problems leveraging their data curation and ML expertise.

The book is endorsed by leading ML experts from both academia and industry. It has forewords by:

• Julian McAuley, Associate Professor at University of California San Diego
• Laurence Moroney, Lead Artificial Intelligence Advocate at Google
• Mengting Wan, Senior Applied Scientist at Microsoft